Overview

Dataset statistics

Number of variables14
Number of observations1408
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory154.1 KiB
Average record size in memory112.1 B

Variable types

Numeric13
Categorical1

Alerts

CIITA is highly overall correlated with CD244 and 8 other fieldsHigh correlation
CD244 is highly overall correlated with CIITA and 8 other fieldsHigh correlation
SLC22A4 is highly overall correlated with CIITA and 7 other fieldsHigh correlation
NFKBIL1 is highly overall correlated with CIITA and 7 other fieldsHigh correlation
IL6 is highly overall correlated with CRPHigh correlation
CD86 is highly overall correlated with CIITA and 7 other fieldsHigh correlation
IRF8 is highly overall correlated with CIITA and 8 other fieldsHigh correlation
CRP is highly overall correlated with CIITA and 5 other fieldsHigh correlation
IL6ST is highly overall correlated with CIITA and 8 other fieldsHigh correlation
TLR1 is highly overall correlated with CIITA and 8 other fieldsHigh correlation
IL1B is highly overall correlated with CIITA and 7 other fieldsHigh correlation
SLC22A4 has 28 (2.0%) zerosZeros
NFKBIL1 has 38 (2.7%) zerosZeros
IL10 has 183 (13.0%) zerosZeros
IL6 has 232 (16.5%) zerosZeros
TNF has 246 (17.5%) zerosZeros
CRP has 676 (48.0%) zerosZeros
IL1B has 15 (1.1%) zerosZeros

Reproduction

Analysis started2023-09-22 07:15:48.381557
Analysis finished2023-09-22 07:16:40.559085
Duration52.18 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

CIITA
Real number (ℝ)

Distinct1291
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean734.6374
Minimum0
Maximum16273
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:40.748111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.988569
Q19.0012525
median131.5
Q3966.25
95-th percentile2300.05
Maximum16273
Range16273
Interquartile range (IQR)957.24875

Descriptive statistics

Standard deviation1548.0874
Coefficient of variation (CV)2.107281
Kurtosis31.723037
Mean734.6374
Median Absolute Deviation (MAD)128.0317
Skewness5.0515258
Sum1034369.5
Variance2396574.8
MonotonicityNot monotonic
2023-09-22T07:16:41.062893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 4
 
0.3%
29 4
 
0.3%
1088 4
 
0.3%
968 4
 
0.3%
48 3
 
0.2%
1117 3
 
0.2%
967 3
 
0.2%
669 3
 
0.2%
582 3
 
0.2%
1043 3
 
0.2%
Other values (1281) 1374
97.6%
ValueCountFrequency (%)
0 2
0.1%
2 1
0.1%
2.2 1
0.1%
2.23527477 1
0.1%
2.240895301 1
0.1%
2.612962234 1
0.1%
2.633043877 1
0.1%
2.645358699 1
0.1%
2.646137493 1
0.1%
2.702497507 1
0.1%
ValueCountFrequency (%)
16273 1
0.1%
14274 1
0.1%
12790 1
0.1%
12219 1
0.1%
12185 1
0.1%
11729 1
0.1%
11489 1
0.1%
11393 1
0.1%
10977 1
0.1%
10610 1
0.1%

CD244
Real number (ℝ)

Distinct1085
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.11774
Minimum0
Maximum947
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:41.362110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.7344173
Q18.9219075
median24
Q3310
95-th percentile501.3
Maximum947
Range947
Interquartile range (IQR)301.07809

Descriptive statistics

Standard deviation188.6678
Coefficient of variation (CV)1.185712
Kurtosis0.22692759
Mean159.11774
Median Absolute Deviation (MAD)22
Skewness1.0220554
Sum224037.77
Variance35595.539
MonotonicityNot monotonic
2023-09-22T07:16:41.650378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9
 
0.6%
0 9
 
0.6%
317 7
 
0.5%
286 6
 
0.4%
3 6
 
0.4%
338 5
 
0.4%
4 5
 
0.4%
310 5
 
0.4%
407 5
 
0.4%
5 4
 
0.3%
Other values (1075) 1347
95.7%
ValueCountFrequency (%)
0 9
0.6%
0.6 1
 
0.1%
0.9 1
 
0.1%
1 4
0.3%
2 9
0.6%
2.4 1
 
0.1%
2.861615128 1
 
0.1%
2.939046488 1
 
0.1%
2.959509632 1
 
0.1%
3 6
0.4%
ValueCountFrequency (%)
947 1
0.1%
921 1
0.1%
858 1
0.1%
831 1
0.1%
816 1
0.1%
792 1
0.1%
784 1
0.1%
755 1
0.1%
752 1
0.1%
746 1
0.1%

SLC22A4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct951
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.06235
Minimum0
Maximum1101.993
Zeros28
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:41.992559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18.6711825
median10
Q3150.25
95-th percentile316.88859
Maximum1101.993
Range1101.993
Interquartile range (IQR)141.57882

Descriptive statistics

Standard deviation116.11026
Coefficient of variation (CV)1.3650018
Kurtosis6.3696431
Mean85.06235
Median Absolute Deviation (MAD)8
Skewness1.9899864
Sum119767.79
Variance13481.592
MonotonicityNot monotonic
2023-09-22T07:16:43.128406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
2.0%
2 21
 
1.5%
5 12
 
0.9%
1 12
 
0.9%
10 10
 
0.7%
3 9
 
0.6%
4 7
 
0.5%
148 7
 
0.5%
151 7
 
0.5%
80 6
 
0.4%
Other values (941) 1289
91.5%
ValueCountFrequency (%)
0 28
2.0%
1 12
0.9%
1.3 2
 
0.1%
1.481802615 1
 
0.1%
1.5 2
 
0.1%
1.599056188 1
 
0.1%
1.6 1
 
0.1%
1.724533783 1
 
0.1%
1.833757158 1
 
0.1%
1.840958922 1
 
0.1%
ValueCountFrequency (%)
1101.993 1
0.1%
632 1
0.1%
630 1
0.1%
607 1
0.1%
601 1
0.1%
596 1
0.1%
584.5356 1
0.1%
579 1
0.1%
539.6863 1
0.1%
502.1353 1
0.1%

NFKBIL1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct855
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.201059
Minimum0
Maximum840
Zeros38
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:43.627925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2064644
Q17.367265
median8.525245
Q377
95-th percentile256.85
Maximum840
Range840
Interquartile range (IQR)69.632735

Descriptive statistics

Standard deviation94.819096
Coefficient of variation (CV)1.6291644
Kurtosis13.021001
Mean58.201059
Median Absolute Deviation (MAD)8.525245
Skewness3.2491362
Sum81947.091
Variance8990.661
MonotonicityNot monotonic
2023-09-22T07:16:44.176297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
2.7%
3 12
 
0.9%
66 12
 
0.9%
75 11
 
0.8%
67 11
 
0.8%
58 11
 
0.8%
27 10
 
0.7%
88 10
 
0.7%
2 9
 
0.6%
71 9
 
0.6%
Other values (845) 1275
90.6%
ValueCountFrequency (%)
0 38
2.7%
1 7
 
0.5%
2 9
 
0.6%
3 12
 
0.9%
4 4
 
0.3%
4.05885 1
 
0.1%
4.480605293 1
 
0.1%
4.490739387 1
 
0.1%
4.520660333 1
 
0.1%
4.534246651 1
 
0.1%
ValueCountFrequency (%)
840 1
0.1%
662 1
0.1%
655 1
0.1%
624 1
0.1%
562 1
0.1%
556 1
0.1%
550 1
0.1%
543 1
0.1%
528 1
0.1%
521 1
0.1%

IL10
Real number (ℝ)

Distinct751
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.775064
Minimum0
Maximum662
Zeros183
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:44.662532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33.7159192
95-th percentile146
Maximum662
Range662
Interquartile range (IQR)1.7159192

Descriptive statistics

Standard deviation58.748648
Coefficient of variation (CV)3.3051159
Kurtosis36.653448
Mean17.775064
Median Absolute Deviation (MAD)1
Skewness5.3853573
Sum25027.291
Variance3451.4037
MonotonicityNot monotonic
2023-09-22T07:16:45.207954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 183
 
13.0%
1 115
 
8.2%
2 89
 
6.3%
3 72
 
5.1%
4 54
 
3.8%
5 41
 
2.9%
7 20
 
1.4%
6 19
 
1.3%
9 19
 
1.3%
8 10
 
0.7%
Other values (741) 786
55.8%
ValueCountFrequency (%)
0 183
13.0%
1 115
8.2%
1.2 2
 
0.1%
1.664834272 1
 
0.1%
1.756840734 1
 
0.1%
1.845253836 1
 
0.1%
1.876663054 1
 
0.1%
1.904045683 1
 
0.1%
1.914583999 1
 
0.1%
1.918726793 1
 
0.1%
ValueCountFrequency (%)
662 1
0.1%
626 1
0.1%
556 1
0.1%
469 1
0.1%
418 2
0.1%
416 1
0.1%
392 1
0.1%
386 1
0.1%
363 1
0.1%
351 1
0.1%

IL6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct711
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.346915
Minimum0
Maximum1095
Zeros232
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:45.738615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4.2172335
Q34.940115
95-th percentile47.3
Maximum1095
Range1095
Interquartile range (IQR)3.940115

Descriptive statistics

Standard deviation66.766382
Coefficient of variation (CV)4.6537101
Kurtosis141.10512
Mean14.346915
Median Absolute Deviation (MAD)1.6225494
Skewness10.797588
Sum20200.456
Variance4457.7498
MonotonicityNot monotonic
2023-09-22T07:16:46.290534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 232
 
16.5%
1 140
 
9.9%
2 93
 
6.6%
3 64
 
4.5%
4 52
 
3.7%
5 33
 
2.3%
6 20
 
1.4%
7 16
 
1.1%
9 10
 
0.7%
10 9
 
0.6%
Other values (701) 739
52.5%
ValueCountFrequency (%)
0 232
16.5%
1 140
9.9%
2 93
6.6%
2.336136337 1
 
0.1%
2.354850319 1
 
0.1%
2.397573993 1
 
0.1%
2.420337337 1
 
0.1%
2.450539629 1
 
0.1%
2.466728947 1
 
0.1%
2.467053356 1
 
0.1%
ValueCountFrequency (%)
1095 1
0.1%
991 1
0.1%
893 1
0.1%
879 1
0.1%
828 1
0.1%
474 1
0.1%
449 1
0.1%
334 2
0.1%
314 1
0.1%
282 1
0.1%

TNF
Real number (ℝ)

Distinct875
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.695409
Minimum0
Maximum1253
Zeros246
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:46.788774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.0066973
median6.876295
Q380
95-th percentile269.65
Maximum1253
Range1253
Interquartile range (IQR)74.993303

Descriptive statistics

Standard deviation110.65756
Coefficient of variation (CV)1.9179612
Kurtosis20.894946
Mean57.695409
Median Absolute Deviation (MAD)6.876295
Skewness3.725991
Sum81235.136
Variance12245.095
MonotonicityNot monotonic
2023-09-22T07:16:47.331528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 246
 
17.5%
3 12
 
0.9%
66 9
 
0.6%
2 8
 
0.6%
1 8
 
0.6%
92 7
 
0.5%
65 7
 
0.5%
91 7
 
0.5%
77 6
 
0.4%
107 6
 
0.4%
Other values (865) 1092
77.6%
ValueCountFrequency (%)
0 246
17.5%
1 8
 
0.6%
2 8
 
0.6%
2.709910252 1
 
0.1%
2.834179147 1
 
0.1%
2.85086859 1
 
0.1%
2.863840117 1
 
0.1%
2.904160367 1
 
0.1%
2.907594642 1
 
0.1%
2.963621792 1
 
0.1%
ValueCountFrequency (%)
1253 1
0.1%
911 1
0.1%
854 1
0.1%
823 1
0.1%
735 1
0.1%
734 1
0.1%
672.574 1
0.1%
607.3479 1
0.1%
582.0322 1
0.1%
556.3941 1
0.1%

CD86
Real number (ℝ)

Distinct1134
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.58538
Minimum0
Maximum4702
Zeros13
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:47.827270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7479537
Q18.116865
median16
Q3391.25
95-th percentile1018.8892
Maximum4702
Range4702
Interquartile range (IQR)383.13313

Descriptive statistics

Standard deviation492.9651
Coefficient of variation (CV)1.7823253
Kurtosis22.698272
Mean276.58538
Median Absolute Deviation (MAD)15
Skewness4.0472597
Sum389432.21
Variance243014.59
MonotonicityNot monotonic
2023-09-22T07:16:48.366822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
0.9%
2 9
 
0.6%
1 8
 
0.6%
4 8
 
0.6%
3 8
 
0.6%
261 7
 
0.5%
8 6
 
0.4%
373 5
 
0.4%
371 5
 
0.4%
7 5
 
0.4%
Other values (1124) 1334
94.7%
ValueCountFrequency (%)
0 13
0.9%
1 8
0.6%
1.1 1
 
0.1%
2 9
0.6%
2.2 1
 
0.1%
2.210320551 1
 
0.1%
2.3 1
 
0.1%
2.310190143 1
 
0.1%
2.34985023 1
 
0.1%
2.367257931 1
 
0.1%
ValueCountFrequency (%)
4702 1
0.1%
4273 1
0.1%
4173 1
0.1%
4006 1
0.1%
3710 1
0.1%
3551 1
0.1%
3519 1
0.1%
3347 1
0.1%
3180 1
0.1%
3169 1
0.1%

IRF8
Real number (ℝ)

Distinct1184
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.30073
Minimum0
Maximum7716
Zeros7
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:48.897062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.3682697
Q19.0822
median49
Q3581
95-th percentile1450.15
Maximum7716
Range7716
Interquartile range (IQR)571.9178

Descriptive statistics

Standard deviation769.15479
Coefficient of variation (CV)1.8300106
Kurtosis23.742988
Mean420.30073
Median Absolute Deviation (MAD)47.8
Skewness4.1920773
Sum591783.42
Variance591599.09
MonotonicityNot monotonic
2023-09-22T07:16:49.485334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
0.5%
3 5
 
0.4%
9 5
 
0.4%
6 5
 
0.4%
459 4
 
0.3%
405 4
 
0.3%
390 4
 
0.3%
2 4
 
0.3%
5 4
 
0.3%
595 4
 
0.3%
Other values (1174) 1362
96.7%
ValueCountFrequency (%)
0 7
0.5%
1 3
0.2%
1.2 2
 
0.1%
2 4
0.3%
2.2 2
 
0.1%
2.7 1
 
0.1%
3 5
0.4%
3.474913665 1
 
0.1%
3.6 1
 
0.1%
3.649361146 1
 
0.1%
ValueCountFrequency (%)
7716 1
0.1%
6842 1
0.1%
6440 1
0.1%
5820 1
0.1%
5498 1
0.1%
5430 1
0.1%
5373 1
0.1%
5260 1
0.1%
5242 1
0.1%
4782 1
0.1%

CRP
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct655
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5593974
Minimum0
Maximum208.349
Zeros676
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:50.011968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34.6855717
95-th percentile5.0689353
Maximum208.349
Range208.349
Interquartile range (IQR)4.6855717

Descriptive statistics

Standard deviation22.271961
Coefficient of variation (CV)4.0061826
Kurtosis42.275741
Mean5.5593974
Median Absolute Deviation (MAD)1
Skewness6.4997085
Sum7827.6315
Variance496.04024
MonotonicityNot monotonic
2023-09-22T07:16:50.524038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 676
48.0%
1 32
 
2.3%
2 20
 
1.4%
3 13
 
0.9%
4 8
 
0.6%
5 7
 
0.5%
4.65778 2
 
0.1%
6 2
 
0.1%
2.673603088 2
 
0.1%
4.69688 1
 
0.1%
Other values (645) 645
45.8%
ValueCountFrequency (%)
0 676
48.0%
1 32
 
2.3%
1.74471 1
 
0.1%
1.79956 1
 
0.1%
1.82788 1
 
0.1%
1.88307 1
 
0.1%
1.88724 1
 
0.1%
1.89073 1
 
0.1%
1.91655 1
 
0.1%
1.93559 1
 
0.1%
ValueCountFrequency (%)
208.349 1
0.1%
195.7681 1
0.1%
189.9426 1
0.1%
185.5627 1
0.1%
172.9744 1
0.1%
170.3398 1
0.1%
164.8793 1
0.1%
163.953 1
0.1%
163.4382 1
0.1%
161.2777 1
0.1%

IL6ST
Real number (ℝ)

Distinct1234
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean575.84543
Minimum3
Maximum13173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:51.020071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.3953005
Q17.6072075
median125
Q3534.0832
95-th percentile3050.65
Maximum13173
Range13170
Interquartile range (IQR)526.47599

Descriptive statistics

Standard deviation1438.7611
Coefficient of variation (CV)2.4985197
Kurtosis21.79402
Mean575.84543
Median Absolute Deviation (MAD)118.46149
Skewness4.450883
Sum810790.36
Variance2070033.5
MonotonicityNot monotonic
2023-09-22T07:16:51.553487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 5
 
0.4%
642 5
 
0.4%
128 4
 
0.3%
257 4
 
0.3%
342 4
 
0.3%
204 4
 
0.3%
781 4
 
0.3%
8 4
 
0.3%
14 4
 
0.3%
187 3
 
0.2%
Other values (1224) 1367
97.1%
ValueCountFrequency (%)
3 3
0.2%
4 1
 
0.1%
4.634 1
 
0.1%
4.73954 1
 
0.1%
4.7527 1
 
0.1%
5 3
0.2%
5.05806 1
 
0.1%
5.14026 1
 
0.1%
5.18142 1
 
0.1%
5.21822 1
 
0.1%
ValueCountFrequency (%)
13173 1
0.1%
12292 1
0.1%
10056 1
0.1%
9528 1
0.1%
9420 1
0.1%
9111 1
0.1%
8958 1
0.1%
8388 1
0.1%
8283 1
0.1%
8268 1
0.1%

TLR1
Real number (ℝ)

Distinct1233
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean379.68498
Minimum0
Maximum3902
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:52.066301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8684006
Q110.82186
median52
Q3700.25
95-th percentile1424.9
Maximum3902
Range3902
Interquartile range (IQR)689.42814

Descriptive statistics

Standard deviation531.10171
Coefficient of variation (CV)1.3987957
Kurtosis2.7039337
Mean379.68498
Median Absolute Deviation (MAD)46.320492
Skewness1.5966889
Sum534596.45
Variance282069.03
MonotonicityNot monotonic
2023-09-22T07:16:52.632036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 7
 
0.5%
58 6
 
0.4%
0 6
 
0.4%
29 5
 
0.4%
27 4
 
0.3%
275 4
 
0.3%
42 4
 
0.3%
32 4
 
0.3%
812 3
 
0.2%
512 3
 
0.2%
Other values (1223) 1362
96.7%
ValueCountFrequency (%)
0 6
0.4%
1 2
 
0.1%
2 1
 
0.1%
3 2
 
0.1%
3.494017613 1
 
0.1%
3.7 1
 
0.1%
3.916544231 1
 
0.1%
3.983130431 1
 
0.1%
4 2
 
0.1%
4.167650655 1
 
0.1%
ValueCountFrequency (%)
3902 1
0.1%
2645 1
0.1%
2642 1
0.1%
2575 1
0.1%
2571 1
0.1%
2488 1
0.1%
2370 1
0.1%
2308 1
0.1%
2248 1
0.1%
2242 1
0.1%

IL1B
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1142
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.72533
Minimum0
Maximum3050.831
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-22T07:16:53.151098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2667794
Q17.792305
median12.478381
Q3449.25
95-th percentile978
Maximum3050.831
Range3050.831
Interquartile range (IQR)441.4577

Descriptive statistics

Standard deviation370.11777
Coefficient of variation (CV)1.4880582
Kurtosis6.5088644
Mean248.72533
Median Absolute Deviation (MAD)9.3862888
Skewness2.1189552
Sum350205.26
Variance136987.17
MonotonicityNot monotonic
2023-09-22T07:16:53.679803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
1.1%
7 13
 
0.9%
4 9
 
0.6%
1 9
 
0.6%
13 9
 
0.6%
2 8
 
0.6%
12 8
 
0.6%
3 8
 
0.6%
6 7
 
0.5%
8 6
 
0.4%
Other values (1132) 1316
93.5%
ValueCountFrequency (%)
0 15
1.1%
1 9
0.6%
1.1 1
 
0.1%
2 8
0.6%
2.3 1
 
0.1%
2.849719878 1
 
0.1%
2.900108169 1
 
0.1%
2.942933824 1
 
0.1%
2.946351365 1
 
0.1%
2.977709634 1
 
0.1%
ValueCountFrequency (%)
3050.831 1
0.1%
2483.771 1
0.1%
2249 1
0.1%
2136.71 1
0.1%
2123 1
0.1%
2100.553 1
0.1%
1938.524 1
0.1%
1891 1
0.1%
1858 1
0.1%
1807.944 1
0.1%

Outcomes
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
1
892 
0
516 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1408
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 892
63.4%
0 516
36.6%

Length

2023-09-22T07:16:54.200116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T07:16:54.580886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 892
63.4%
0 516
36.6%

Most occurring characters

ValueCountFrequency (%)
1 892
63.4%
0 516
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1408
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 892
63.4%
0 516
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 892
63.4%
0 516
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 892
63.4%
0 516
36.6%

Interactions

2023-09-22T07:16:37.189003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:49.778834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:53.670425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:57.229039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:01.591193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:05.605718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:09.765194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:14.060305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:18.234362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:22.369120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:26.769412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:31.188291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:34.361698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:37.414414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:49.997602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:54.003894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:57.574399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:01.922869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:05.942260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:09.995333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:14.404217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:18.571580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:22.715567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:27.114207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:31.525312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:34.597512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:37.607530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:50.196322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:54.291888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:57.886230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:02.183763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:06.250138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:10.300861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:14.709632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:18.871034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:23.047673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:27.419523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:31.831527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:34.799132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:37.806783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:50.397220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:54.473428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:58.197577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:02.489712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:06.558684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:10.604473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:15.025384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:19.186575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:23.382450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:27.723688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:32.175169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:35.023874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:38.002313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:50.592911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:54.650948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:58.499665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:02.786439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:06.865352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:10.875934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:15.334701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:19.484457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:23.699808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:28.026670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:32.481759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:35.234725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:38.217182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:50.922971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:54.857128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:58.819334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:03.106968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:07.187876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:11.201705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:15.654859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:19.802267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:24.055016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:28.664623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:32.682609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:35.451630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:38.414127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:51.259747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:55.066278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:59.355179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:03.407492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:07.497941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:11.514387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:15.974130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:20.122231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:24.388192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:28.970275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:32.881488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:35.662285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:38.620993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:51.599890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:55.319527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:59.670479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:03.719931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:07.815247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:11.835603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:16.292795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:20.443704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:24.728235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:29.289205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:33.097189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:35.879694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:38.808779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:51.907822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:55.622467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:59.972683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:04.023460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:08.134879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:12.165238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:16.607167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:20.745153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:25.061332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:29.584259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:33.288293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:36.090608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:39.043783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:52.313027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:55.956162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:00.307761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:04.352378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:08.477318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:12.761291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:16.940449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:21.087415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:25.418279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:29.910784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:33.509766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:36.322946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:39.235939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:52.625574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:56.252683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:00.607732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:04.647998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:08.783461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:13.079672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:17.252259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:21.392403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:25.739852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:30.209651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:33.708584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:36.524534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:39.445627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:52.974743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:56.563093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:00.928236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:04.960523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:09.111806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:13.399775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:17.570089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:21.705243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:26.089543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:30.524294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:33.913554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:36.737608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:39.664278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:53.338676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:15:56.901281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:01.269627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:05.289679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:09.446875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:13.732820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:17.909248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:22.049342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:26.435708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:30.859528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:34.149249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T07:16:36.967959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-22T07:16:54.855811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CIITACD244SLC22A4NFKBIL1IL10IL6TNFCD86IRF8CRPIL6STTLR1IL1BOutcomes
CIITA1.0000.8330.7130.8330.048-0.1200.3990.8470.877-0.6440.7870.8830.7250.111
CD2440.8331.0000.7940.8270.055-0.1770.2890.8610.841-0.5840.7000.8500.8250.298
SLC22A40.7130.7941.0000.8290.024-0.1000.2420.7510.766-0.4060.6060.8390.7700.125
NFKBIL10.8330.8270.8291.0000.1940.0520.3940.8680.851-0.3990.6770.8400.7820.155
IL100.0480.0550.0240.1941.0000.4550.3550.2710.1630.2700.088-0.0110.1390.103
IL6-0.120-0.177-0.1000.0520.4551.0000.3560.0620.0050.567-0.195-0.213-0.0650.043
TNF0.3990.2890.2420.3940.3550.3561.0000.4520.374-0.0100.3310.3030.2910.140
CD860.8470.8610.7510.8680.2710.0620.4521.0000.912-0.4490.7240.8120.8050.164
IRF80.8770.8410.7660.8510.1630.0050.3740.9121.000-0.5250.7770.8220.7850.166
CRP-0.644-0.584-0.406-0.3990.2700.567-0.010-0.449-0.5251.000-0.641-0.633-0.4700.000
IL6ST0.7870.7000.6060.6770.088-0.1950.3310.7240.777-0.6411.0000.7190.6040.166
TLR10.8830.8500.8390.840-0.011-0.2130.3030.8120.822-0.6330.7191.0000.8330.170
IL1B0.7250.8250.7700.7820.139-0.0650.2910.8050.785-0.4700.6040.8331.0000.287
Outcomes0.1110.2980.1250.1550.1030.0430.1400.1640.1660.0000.1660.1700.2871.000

Missing values

2023-09-22T07:16:39.980168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-22T07:16:40.404335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CIITACD244SLC22A4NFKBIL1IL10IL6TNFCD86IRF8CRPIL6STTLR1IL1BOutcomes
0726.0338.0206.080.07.02.00.0373.0482.00.0426.0930.0763.01
1774.0229.0114.043.02.03.00.0271.0650.00.0630.0666.0354.00
21709.0705.0601.0149.03.04.00.0591.01042.00.0930.01671.01000.01
3847.0487.083.0113.02.04.00.0464.01081.00.0749.0509.0354.00
4288.0317.0188.068.06.00.00.0358.0358.00.0315.0659.0691.01
5579.0413.0101.081.00.01.00.0407.0678.00.0704.0508.0381.00
61004.0415.0158.097.00.00.00.0661.0665.00.0465.0753.0446.01
7992.0352.0144.062.01.02.00.0304.0678.00.01085.0661.0404.00
8585.0278.077.058.01.00.00.0408.0397.00.0770.0491.0430.01
9938.0262.0152.073.00.01.00.0522.0776.00.0922.0742.0488.00
CIITACD244SLC22A4NFKBIL1IL10IL6TNFCD86IRF8CRPIL6STTLR1IL1BOutcomes
1398590.097.010.030.05.07.0178.0217.0320.02.0477.085.0113.00
1399525.0125.029.023.014.01.0198.0348.0342.02.0204.0111.0250.00
14001210.0174.010.022.035.05.079.0359.0445.00.0328.082.0255.00
14011773.0144.08.019.02.09.063.0136.0517.00.0430.081.0133.00
14021260.0115.011.034.035.015.0225.0479.0680.00.0128.0111.0228.00
1403892.0159.037.021.015.02.058.0207.0423.00.0387.0113.059.00
14041295.0181.023.029.034.03.0181.0374.0598.00.0218.0167.0344.00
1405600.0174.05.038.041.01.065.0494.0348.02.0165.073.0135.00
1406414.0122.010.031.068.00.0115.0216.0415.00.0146.027.040.00
14071145.0239.014.039.010.02.0269.0505.0613.00.0212.0119.0248.00